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Course Outline
Module 1: Context, Scope, and Delivery Challenges
- Differentiating between autocomplete assistance and autonomous multi-step execution
- Common misconceptions regarding artificial intelligence in software delivery
- Limitations of relying solely on prompt engineering
- Assessment of participant tooling, operational pain points, and strategic objectives
- Selecting appropriate AI operating models for engineering teams for government
Module 2: Specification Ingestion and Structured Decomposition
- Establishing a structural inventory of stakeholder documentation
- Techniques for requirement extraction
- Chunking strategies: structural, semantic, and sliding-window methods
- Maintaining dependencies and cross-references
- Processing tables, diagrams, flowcharts, and mixed input formats
- Effective management of context windows
Module 3: Human Judgment Boundaries
- Areas where human decision-making remains essential
- Identification of hallucinated dependencies
- Detection of fabricated constraints and inverted logic
- Mitigation of unsafe default responses
- Validation frameworks ensuring traceability, consistency, and completeness
Module 4: From Requirements to Code with Agentic Tools
- Architecture-first delivery methodologies
- Component mapping and service boundary definition
- Utilization of API contracts as delivery anchors
- Implementation of persistent rules and constraints within AI tools
- Alignment of task instructions with requirements
- Comparison of minimal prompting versus constrained prompting approaches
- Contract-first generation for backend and frontend systems
Module 5: Agentic Iteration Loop
- The self-correction spiral
- Controlled iterative delivery cycles
- Review of diffs and code changes
- Detection of scope creep and unauthorized modifications
- Management of limited context memory
- Leveraging iteration history for continuous improvement
Module 6: Code Quality Enforcement
- Prompt constraints for edge cases
- Rules documents as living governance artifacts
- Automated gates utilizing linting and static analysis
- Security scanning in AI-generated code
- Dependency and architecture conformance checks
- Human review protocol for AI outputs
Module 7: Feedback Loops and Continuous Improvement
- Incorporating structured failures into AI workflows
- Bounded iterations and stop criteria
- Logging cycles and outcomes
- Improving rules documents over time
- Building reusable engineering intelligence
Module 8: Security Anti-Patterns in AI Delivery
- Common security risks in generated code
- Technology-specific security rules appendices
- Pre-commit security scanning
- Secure SDLC controls for AI-assisted development
- Human accountability in secure delivery for government
Module 9: Testing Anchored to Specifications
- Generating test specifications from requirements
- Domain-language test design
- Safe generation of test implementations
- Mutation testing concepts
- Specification coverage validation
- Assertion-strength review
- Diagnostic questioning models
Module 10: Maintaining the System
- Living artifacts: contracts, maps, rules, test specs
- Evolving constraints over time
- AI governance for long-term maintainability
- Technical debt prevention using AI controls
- Operating model for sustainable AI engineering teams for government
Requirements
Individuals seeking enrollment must demonstrate proficiency in the following areas:
* Demonstrated history of managing software development initiatives.
* Competence in core application architecture principles.
* Working knowledge of API integration, backend or frontend infrastructure, and full-stack delivery methodologies.
* Familiarity with Agile frameworks or iterative release cycles.
* Fundamental understanding of quality assurance and software testing protocols.
While prior exposure to artificial intelligence-assisted coding platforms is advantageous, it is not a prerequisite. This curriculum is designed for mid-level through senior technical personnel responsible for delivering solutions for government.
14 Hours
